Time Frame Selection Based Feature Extraction for Fire Detection in Video Surveillance

This research proposed new simple feature extraction method to characterize the feature of fire that capable to be used in classifying an object as fire or neither in video surveillance for fire detection. The process of extraction feature consists with simple segmentation process in color domain, and the movement. Time frame selection is proposed to select specific video frames that will be extracted and will be placed as key feature or attribute by calculate the number of binary histogram level. We using classification method Back-Propagation Neural Network to classify the features that has been generates and evaluates its accuracy. The result of this experiment has showed the performance of method could give accuracy until 76.67% in classifying video fire detection.